read_edgelist(path, comments='#', delimiter=None, create_using=None, nodetype=None, data=True, edgetype=None, encoding='utf-8')
Since nodes must be hashable, the function nodetype must return hashable types (e.g. int, float, str, frozenset - or tuples of those, etc.)
File or filename to read. If a file is provided, it must be opened in 'rb' mode. Filenames ending in .gz or .bz2 will be uncompressed.
The character used to indicate the start of a comment.
The string used to separate values. The default is whitespace.
Use specified container to build graph. The default is networkx.Graph, an undirected graph.
Convert node data from strings to specified type
Tuples specifying dictionary key names and types for edge data
Convert edge data from strings to specified type and use as 'weight'
Specify which encoding to use when reading file.
A networkx Graph or other type specified with create_using
Read a bipartite graph from a list of edges.
>>> from networkx.algorithms import bipartite
... G = nx.path_graph(4)
... G.add_nodes_from([0, 2], bipartite=0)
... G.add_nodes_from([1, 3], bipartite=1)
... bipartite.write_edgelist(G, "test.edgelist")
... G = bipartite.read_edgelist("test.edgelist")
>>> fh = open("test.edgelist", "rb")
... G = bipartite.read_edgelist(fh)
... fh.close()
>>> G = bipartite.read_edgelist("test.edgelist", nodetype=int)
Edgelist with data in a list:
>>> textline = "1 2 3"
... fh = open("test.edgelist", "w")
... d = fh.write(textline)
... fh.close()
... G = bipartite.read_edgelist(
... "test.edgelist", nodetype=int, data=(("weight", float),)
... )
... list(G) [1, 2]
>>> list(G.edges(data=True)) [(1, 2, {'weight': 3.0})]
See parse_edgelist() for more examples of formatting.
See :Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them